There have been significant strides in improving the set of available tools for constructing reproducible analyses such as the concept and implementation of literate computing, version control systems, and mandates by journals and sponsors that create standards for submitting data, code, and systems for reproducibility. Furthermore, transparency and reproducibility are deemed necessary by the American Statistical Association’s Ethical Guidelines for Statistical Practice ( American Statistical Association, 2016). While some of the impetus for the interest in reproducibility has come from high-profile incidents that reached the sphere of the general public ( Carroll, 2017), there is a consensus that reproducibility is fundamental to scientific communication and to the acceleration of the scientific process ( Wilkinson et al., 2016). Reproducibility, accountability and transparency are increasingly accepted as core principles of science and statistics. Reproducing collaborative work may be highly complex, requiring repeating computations on multiple systems from multiple authors however, determining the provenance of each unit is simpler, requiring only a search using file hashes and version control systems. However, accountable units use file hashes and do not involve watermarking or public repositories like VCRs. Both accountable units and VCRs are version controlled, sharable, and can be incorporated into a collaborative project. An accountable unit is a data file (statistic, table or graphic) that can be associated with a provenance, meaning how it was created, when it was created and who created it, and this is similar to the ‘verifiable computational results’ (VCR) concept proposed by Gavish and Donoho. To this end, we have developed a system, R package, and R Shiny application called adapr (Accountable Data Analysis Process in R) that is built on the principle of accountable units. ![]() This implies a need for computing systems and environments that can efficiently satisfy reproducibility and accountability standards. ![]() ![]() Efficiently producing transparent analyses may be difficult for beginners or tedious for the experienced.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |